Prediction Of Individual Outcome Research Articles

Uncertainty evaluation of image-based tumour control probability models in radiotherapy of prostate cancer using a visual analytic tool.

Functional imaging techniques provide radiobiological information that can be included into tumour control probability (TCP) models to enable individualized outcome predictions in radiotherapy. However, functional imaging and the derived radiobiological information are influenced by uncertainties, translating into variations in individual TCP predictions. In this study we applied a previously developed analytical tool to quantify dose and TCP uncertainty bands when initial cell density is estimated from MRI-based apparent diffusion coefficient maps of eleven patients. TCP uncertainty bands of 16% were observed at patient level, while dose variations bands up to 8 Gy were found at voxel level for an iso-TCP approach.

Shanghai Score: A Prognostic and Adjuvant Treatment-evaluating System Constructed for Chinese Patients with Hepatocellular Carcinoma after Curative Resection.

Background:For Chinese patients with hepatocellular carcinoma (HCC), surgical resection is the most important treatment to achieve long-term survival for patients with an early-stage tumor, and yet the prognosis after surgery is diverse. We aimed to construct a scoring system (Shanghai Score) for individualized prognosis estimation and adjuvant treatment evaluation.Methods:A multivariate Cox proportional hazards model was constructed based on 4166 HCC patients undergoing resection during 2001–2008 at Zhongshan Hospital. Age, hepatitis B surface antigen, hepatitis B e antigen, partial thromboplastin time, total bilirubin, alkaline phosphatase, γ-glutamyltransferase, α-fetoprotein, tumor size, cirrhosis, vascular invasion, differentiation, encapsulation, and tumor number were finally retained by a backward step-down selection process with the Akaike information criterion. The Harrell's concordance index (C-index) was used to measure model performance. Shanghai Score is calculated by summing the products of the 14 variable values times each variable's corresponding regression coefficient. Totally 1978 patients from Zhongshan Hospital undergoing resection during 2009–2012, 808 patients from Eastern Hepatobiliary Surgery Hospital during 2008–2010, and 244 patients from Tianjin Medical University Cancer Hospital during 2010–2011 were enrolled as external validation cohorts. Shanghai Score was also implied in evaluating adjuvant treatment choices based on propensity score matching analysis.Results:Shanghai Score showed good calibration and discrimination in postsurgical HCC patients. The bootstrap-corrected C-index (confidence interval [CI]) was 0.74 for overall survival (OS) and 0.68 for recurrence-free survival (RFS) in derivation cohort (4166 patients), and in the three independent validation cohorts, the CIs for OS ranged 0.70–0.72 and that for RFS ranged 0.63–0.68. Furthermore, Shanghai Score provided evaluation for adjuvant treatment choices (transcatheter arterial chemoembolization or interferon-α). The identified subset of patients at low risk could be ideal candidates for curative surgery, and subsets of patients at moderate or high risk could be recommended with possible adjuvant therapies after surgery. Finally, a web server with individualized outcome prediction and treatment recommendation was constructed.Conclusions:Based on the largest cohort up to date, we established Shanghai Score – an individualized outcome prediction system specifically designed for Chinese HCC patients after surgery. The Shanghai Score web server provides an easily accessible tool to stratify the prognosis of patients undergoing liver resection for HCC.

Prediction of individual outcomes for asthma sufferers.

We consider the problem of individual-specific medication level recommendation (initiation, removal, increase, or decrease) for asthma sufferers. Asthma is one of the most common chronic diseases in both adults and children, affecting 8% of the US population and costing $37-63 billion/year in the United States of America. Asthma is a complex disease, whose symptoms may wax and wane, making it difficult for clinicians to predict outcomes and prognosis. Improved ability to predict prognosis can inform decision making and may promote conversations between clinician and provider around optimizing medication therapy. Data from the US Medical Expenditure Panel Survey (MEPS) years 2000-2010 were used to fit a longitudinal model for a multivariate response of adverse events (Emergency Department or in-patient visits, excessive rescue inhaler use, and oral steroid use). To reduce bias in the estimation of medication effects, medication level was treated as a latent process which was restricted to be consistent with prescription refill data. This approach is demonstrated to be effective in the MEPS cohort via predictions on a validation hold out set and a synthetic data simulation study. This framework can be easily generalized to medication decisions for other conditions as well.

Individual outcome prediction for myelodysplastic syndrome (MDS) and secondary acute myeloid leukemia from MDS after allogeneic hematopoietic cell transplantation.

We integrated molecular data with available prognostic factors in patients undergoing allogeneic hematopoietic cell transplantation (alloHCT) for myelodysplastic syndrome (MDS) or secondary acute myeloid leukemia (sAML) from MDS to evaluate their impact on prognosis. Three hundred four patients were sequenced for mutations in 54 genes. We used a Cox multivariate model and competing risk analysis with internal and cross validation to identify factors prognostic of overall survival (OS), cumulative incidence of relapse (CIR), and non-relapse mortality (NRM). In multivariate analysis, mutated NRAS, U2AF1, IDH2, and TP53 and/or a complex karyotype were significant prognostic markers for OS besides age above 60years, remission status, IPSS-R cytogenetic risk, HCT-CI>2 and female donor sex. Mutated NRAS, IDH1, EZH2, and TP53 and/or a complex karyotype were genetic aberrations with prognostic impact on CIR. No molecular markers were associated with the risk of NRM. The inclusion of molecular information results in better risk prediction models for OS and CIR when assessed by the Akaike information criterion. Internal cross validation confirmed the robustness of our comprehensive risk model. In summary, we propose to combine molecular, cytogenetic, and patient- and transplantation-associated risk factors into a comprehensive risk model to provide personalized predictions of outcome after alloHCT.

Establishing High Dimensional Immune Signatures from Peripheral Blood via Mass Cytometry in a Discovery Cohort of Stage IV Melanoma Patients.

The identification of blood-borne biomarkers correlating with melanoma patient survival remains elusive. Novel techniques such as mass cytometry could help to identify melanoma biomarkers, allowing simultaneous detection of up to 100 parameters. However, the evaluation of multiparametric data generated via time-of-flight mass cytometry requires novel analytical techniques because the application of conventional gating strategies currently used in polychromatic flow cytometry is not feasible. In this study, we have employed 38-channel time-of-flight mass cytometry analysis to generate comprehensive immune cell signatures using matrix boolean analysis in a cohort of 28 stage IV melanoma patients and 17 controls. Clusters of parameters were constructed from the abundance of cellular phenotypes significantly different between patients and controls. This approach identified patient-specific combinatorial immune signatures consisting of high-resolution subsets of the T cell, NK cell, B cell, and myeloid compartments. An association with superior survival was characterized by a balanced distribution of myeloid-derived suppressor cell-like and APC-like myeloid phenotypes and differentiated NK cells. The results of this study in a discovery cohort of melanoma patients suggest that multifactorial immune signatures have the potential to allow more accurate prediction of individual patient outcome. Further investigation of the identified immune signatures in a validation cohort is now warranted.

Prognostic value and their clinical implication of 89-gene signature in glioma.

Gliomas are the most common and aggressive primary tumors in adults. The current approaches, such as histological classification and molecular genetics, have limitation in prediction of individual therapeutic outcomes due to heterogeneity within the tumor groups. Recent studies have proposed several gene signatures to predict glioma's prognosis. However, most of the gene expression profiling studies have been performed on relatively small number of patients and combined probes from diverse microarray chips. Here, we identified prognostic 89 common genes from diverse microarray chips. The 89-gene signature classified patients into good and bad prognostic groups which differed in the overall survival significantly, reflecting the biological characteristics and heterogeneity. The robustness and accuracy of the gene signature as an independent prognostic factor was validated in three microarray and one RNA-seq data sets independently. By incorporating into histological classification and molecular marker, the 89-gene signature could further stratify patients with 1p/19q co-deletion and IDH1 mutation. Additionally, subset analyses suggested that the 89-gene signature could predict patients who would benefit from adjuvant chemotherapy. Conclusively, we propose that the 89-gene signature would have an independent and accurate prognostic value for clinical use. This study also offers opportunities for novel targeted treatment of individual patients.

Anticipation of complications after laparoscopic cholecystectomy: prediction of individual outcome

Complication rates after a laparoscopic cholecystectomy are still up to 10%. Knowledge of individual patient risk profiles could help to reduce morbidity. The aim of this study is to create risk profiles for specific complications to anticipate on individual outcome. Individual patient outcome for a specific post-operative complication was assessed from a retrospective database of two major teaching hospitals, using uni- and multivariable analyses. A total of 4359 patients were included of which 346 developed one or more complications (8%). Five risk profiles were found to predict specific complications: older patients (>65year) are at risk for pneumonia (OR 7.0, 95% CI 3.3-15.0, p<0.001) and bleeding (OR 2.2, 95% CI 1.2-3.9, p=0.014), patients with acute cholecystitis are at risk for intra-abdominal abscess (OR 5.9, 95% CI 3.4-10.1, p<0.001), bile leakage (OR 3.6, 95% CI 2.0-6.6, p<0.001) and pneumonia (OR 3.5, 95% CI 1.6-7.6, p<0.002), previous history of cholecystitis is predictive for wound infection (OR 5.1, 95% CI, (2.7-9.7), p<0.001), intra-abdominal abscess (OR 6.1, 95% CI 2.8-13.8, p<0.001), post-operative bleeding (OR 4.8, 95% CI 2.1-11.1, p<0.001), bile leakage (OR 7.2, 95% CI 3.4-15.4, p<0.001) and pneumonia (OR 3.9, 95% CI 1.3-11.9, p=0.018), pre-operative ERCP is predictive for intra-abdominal abscess (OR 3.3, 95% CI 2.0-5.7, p<0.001), post-operative bleeding (OR 2.1, 95% CI 1.2-3.9, p=0.058) and pneumonia (OR 3.8, 95% CI 1.9-7.8, p=0.001), and converted patients are at risk for wound infection (OR 4.0, 95% CI 2.1-7.7, p<0.001) and intra-abdominal abscess (OR 3.5, 95% CI 1.6-7.7, p=0.002). Individual risk prediction of outcome after laparoscopic cholecystectomy is feasible. This facilitates individual pre-operative doctor-patient communication and may tailor surgical strategies.

Predicting Outcomes after Severe and Moderate Traumatic Brain Injury: An External Validation of Impact and Crash Prognostic Models in a Large Spanish Cohort.

Prognostic models that were developed by the International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) study group and the Corticosteroid Randomization After Signification Head injury (CRASH) collaborators are the most commonly used prognostic models for outcome after traumatic brain injury (TBI). Although they have been considered to be useful tools in clinical practice, a continuous process of external validation in recent cohorts of different populations is necessary. The objective of this study was to determine the external validity and compare the IMPACT and CRASH-refitted models for prediction of outcomes after moderate or severe TBI in a non-selected 1301-patient Spanish cohort. We studied discrimination, calibration, and overall fit as external validation measures. Excellent discrimination was indicated (area under the curve [AUC] 0.78-0.87) by the higher values in the validation than in the development sample for both models and outcomes. Calibration revealed that IMPACT models, in general, predict lower probabilities of both outcomes (mortality and disability). In contrast, CRASH-refitted models provided higher predicted probabilities than those observed. We can conclude that both models demonstrate an adequate performance in our representative traumatic brain Mediterranean population. Therefore, these models can be sensibly applied in our clinical practice so long as their limitations are observed during individual outcome prediction.

Clinical Neurochemistry of Subarachnoid Hemorrhage: Toward Predicting Individual Outcomes via Biomarkers of Brain Energy Metabolism.

The functional outcome of patients with subarachnoid hemorrhage is difficult to predict at the individual level. The monitoring of brain energy metabolism has proven to be useful in improving the pathophysiological understanding of subarachnoid hemorrhage. Nonetheless, brain energy monitoring has not yet clearly been included in official guidelines for the management of subarachnoid hemorrhage patients, likely because previous studies compared only biological data between two groups of patients (unfavorable vs favorable outcomes) and did not determine decision thresholds that could be useful in clinical practice. Therefore, this Viewpoint discusses recent findings suggesting that monitoring biomarkers of brain energy metabolism at the level of individuals can be used to predict the outcomes of subarachnoid hemorrhage patients. Indeed, by taking into account specific neurochemical patterns obtained by local or global monitoring of brain energy metabolism, it may become possible to predict routinely, and with sufficient sensitivity and specificity, the individual outcomes of subarachnoid hemorrhage patients. Moreover, combining both local and global monitoring improves the overall performance of individual outcome prediction. Such a combined neurochemical monitoring approach may become, after prospective clinical validation, an important component in the management of subarachnoid hemorrhage patients to adapt individualized therapeutic interventions.

Elevation of Pretreatment Carcinoembryonic Antigen Level as a Prognostic Factor for Colon Cancer: Incorporating a C Stage in the AJCC TNM Classification.

Thirunavukarasu and colleagues1use data from theNational Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database to determine whether preoperative serum carcinoembryonic antigen (CEA) status, dichotomized as elevated (C1) or normal (C0), can become a staging component in colon cancer, the C stage, akin to the more common T, N, M, R, and G stages. In their analysis, the authors reveal thatC1stage is independentlyassociatedwithoverall and disease-specific mortality. Of interest, this effect was strongenough thatupstaging couldoccur, ashappened forpatients with node-negative C1 disease who in some cases had worse outcomes than patients with node-positive C0 disease. These findings are potentially important and reinforce the long-standing2bodyofevidence thatelevation inCEA level can be a stage-independent prodrome of advanced disease. This finding is in line with earlier recommendations of the American Society of Clinical Oncology3 and the European Society for Medical Oncology,4 which call for the incorporation of pretreatment measurement of CEA levels into the standard of care. Even so, it remains crucial to view these findings in the context of SEER as its data source and consider its inherent limitations. This data set does not include the actual CEA values and does not use a cutoff value; rather, the database depends on reporting physician interpretation of what is considered elevated. This is understandable from a clinical practice perspective, where interpreting any laboratory value depends on patient characteristics, but it is obviously subject to error. The authors note that work5 that reports the exact numerical value of preoperative CEA levels was of little value for individual outcome prediction. This limitation leads to caution against presumptions that the prognostic value of CEA level thresholds implies that CEA levels can predict outcomes in a linear way. Consequently, whenever any continuous laboratory value is scrutinized as a potential prognostic factor, it remains preferable to have access to raw numerical data. Use of raw data eliminates individual judgment bias and permits defining a consistent threshold that can be objectively tested as a cutoff value. Use of raw data could also have avoided the concessions the authors had to make, such as having to exclude the borderline elevated CEA level category at the cost of likely exaggerating the contrast between the elevated and normal CEA level categories or the inability to numerically correct for influencers of serum CEA level, such as smoking status or liver disease. Nevertheless, the authors havedemonstrated that preoperative CEA level interpreted as elevated can be a poor prognostic factor across stages. However, if the C stage is to be incorporated into the American Joint Committee on Cancer (AJCC) staging system, there will need to be clearly defined thresholds with confounder mitigation. Once proposed, any cutoff valuewill need to be extensively validated in large, diverse populations using prospective data before we can conclude that they are universally applicable.

Prediction of outcome in patients with low back pain – A prospective cohort study comparing clinicians' predictions with those of the Start Back Tool

The clinical course of low back pain (LBP) cannot be accurately predicted by existing prediction tools. Therefore clinicians rely largely on their experience and clinical judgement. The objectives of this study were to investigate 1) which patient characteristics were associated with chiropractors' expectations of outcome from a LBP episode, 2) if clinicians' expectations related to outcome, 3) how accurate clinical predictions were as compared to those of the STarT Back Screening Tool (SBT), and 4) if accuracy was improved by combining clinicians' expectations and the SBT. Outcomes were measured as LBP intensity (0–10) and disability (RMDQ) after 2-weeks, 3-months, and 12-months. The course of LBP in 859 patients was predicted to be short (54%), prolonged (36%), or chronic (7%). Clinicians' expectations were most strongly associated with education, LBP history, radiating pain, and neurological signs at baseline and related to all outcomes. The accuracies of predictions made by clinicians (AUC .58–.63) and the SBT (AUC .50–.61) were comparable and low. No substantial increase in the predictive capability was achieved by combining clinicians' expectations and the SBT. In conclusion, chiropractors' predictions were associated with well-established prognostic factors but not simply a product of these. Chiropractors were able to predict differences in outcome on a group level, but prediction of individual patients' outcomes were inaccurate and not substantially improved by the SBT. It is worth investigating if more accurate tools can be developed to assist clinicians in prediction of outcome.

Prognostic Value of Pretherapeutic Tumor-to-Blood Standardized Uptake Ratio in Patients with Esophageal Carcinoma.

Despite ongoing efforts to develop new treatment options, the prognosis for patients with inoperable esophageal carcinoma is still poor and the reliability of individual therapy outcome prediction based on clinical parameters is not convincing. The aim of this work was to investigate whether PET can provide independent prognostic information in such a patient group and whether the tumor-to-blood standardized uptake ratio (SUR) can improve the prognostic value of tracer uptake values. (18)F-FDG PET/CT was performed in 130 consecutive patients (mean age ± SD, 63 ± 11 y; 113 men, 17 women) with newly diagnosed esophageal cancer before definitive radiochemotherapy. In the PET images, the metabolically active tumor volume (MTV) of the primary tumor was delineated with an adaptive threshold method. The blood standardized uptake value (SUV) was determined by manually delineating the aorta in the low-dose CT. SUR values were computed as the ratio of tumor SUV and blood SUV. Uptake values were scan-time-corrected to 60 min after injection. Univariate Cox regression and Kaplan-Meier analysis with respect to overall survival (OS), distant metastases-free survival (DM), and locoregional tumor control (LRC) was performed. Additionally, a multivariate Cox regression including clinically relevant parameters was performed. In multivariate Cox regression with respect to OS, including T stage, N stage, and smoking state, MTV- and SUR-based parameters were significant prognostic factors for OS with similar effect size. Multivariate analysis with respect to DM revealed smoking state, MTV, and all SUR-based parameters as significant prognostic factors. The highest hazard ratios (HRs) were found for scan-time-corrected maximum SUR (HR = 3.9) and mean SUR (HR = 4.4). None of the PET parameters was associated with LRC. Univariate Cox regression with respect to LRC revealed a significant effect only for N stage greater than 0 (P = 0.048). PET provides independent prognostic information for OS and DM but not for LRC in patients with locally advanced esophageal carcinoma treated with definitive radiochemotherapy in addition to clinical parameters. Among the investigated uptake-based parameters, only SUR was an independent prognostic factor for OS and DM. These results suggest that the prognostic value of tracer uptake can be improved when characterized by SUR instead of SUV. Further investigations are required to confirm these preliminary results.

Predicting outcome of status epilepticus.

Status epilepticus (SE) is a frequent neurological emergency complicated by high mortality and often poor functional outcome in survivors. The aim of this study was to review available clinical scores to predict outcome. Literature review. PubMed Search terms were "score", "outcome", and "status epilepticus" (April 9th 2015). Publications with abstracts available in English, no other language restrictions, or any restrictions concerning investigated patients were included. Two scores were identified: "Status Epilepticus Severity Score--STESS" and "Epidemiology based Mortality score in SE--EMSE". A comprehensive comparison of test parameters concerning performance, options, and limitations was performed. Epidemiology based Mortality score in SE allows detailed individualization of risk factors and is significantly superior to STESS in a retrospective explorative study. In particular, EMSE is very good at detection of good and bad outcome, whereas STESS detecting bad outcome is limited by a ceiling effect and uncertainty of correct cutoff value. Epidemiology based Mortality score in SE can be adapted to different regions in the world and to advances in medicine, as new data emerge. In addition, we designed a reporting standard for status epilepticus to enhance acquisition and communication of outcome relevant data. A data acquisition sheet used from patient admission in emergency room, from the EEG lab to intensive care unit, is provided for optimized data collection. Status Epilepticus Severity Score is easy to perform and predicts bad outcome, but has a low predictive value for good outcomes. Epidemiology based Mortality score in SE is superior to STESS in predicting good or bad outcome but needs marginally more time to perform. Epidemiology based Mortality score in SE may prove very useful for risk stratification in interventional studies and is recommended for individual outcome prediction. Prospective validation in different cohorts is needed for EMSE, whereas STESS needs further validation in cohorts with a wider range of etiologies. This article is part of a Special Issue entitled "Status Epilepticus".

The renal protective effect of angiotensin receptor blockers depends on intra-individual response variation in multiple risk markers.

Angiotensin receptor blockers (ARBs) are renoprotective and targeted to blood pressure. However, ARBs have multiple other (off-target) effects which may affect renal outcome. It is unknown whether on-target and off-target effects are congruent within individuals. If not, this variation in short term effects may have important implications for the prediction of individual long term renal outcomes. Our aim was to assess intra-individual variability in multiple parameters in response to ARBs in type 2 diabetes. Changes in systolic blood pressure (SBP), albuminuria, potassium, haemoglobin, cholesterol and uric acid after 6 months of losartan treatment were assessed in the RENAAL database. Improvement in predictive performance of renal outcomes (ESRD or doubling serum creatinine) for each individual using ARB-induced changes in all risk markers was assessed by the relative integrative discrimination index (RIDI). SBP response showed high variability (mean -5.7 mmHg, 5(th) to 95(th) percentile -36.5 to +24.0 mmHg) between individuals. Changes in off-target parameters also showed high variability between individuals. No congruency was observed between responses to losartan in multiple parameters within individuals. Using individual responses in all risk markers significantly improved renal risk prediction (RIDI 30.4%, P < 0.01) compared with using only SBP changes. Results were successfully replicated in two independent trials with irbesartan, IDNT and IRMA-2. In this post hoc analysis we showed that ARBs have multiple off-target effects which vary between and within individuals. Combining all ARB-induced responses beyond SBP provides a more accurate prediction of who will benefit from ARB therapy. Prospective trials are required to validate these findings.

Chemokine RANTES/CCL5 as an unknown link between wound healing in the jawbone and systemic disease: is prediction and tailored treatments in the horizon?

BackgroundThis research elucidates the question of whether common and widespread dental procedures (DP) like root filling (RF) and the removal of wisdom teeth (WT) contribute to chronic inflammation in the jawbone. Dentists, in carrying out these DP, can set off defective wound healing in the jawbone in ignorance of its connection to inflammatory mediators and the possibility of it being a hidden cause of chronic systemic diseases (SYD).Materials and methodsWe examined samples of the jawbone for seven cytokines by multiplex analysis in three groups of jawbone areas. In order to clarify systemic interrelations, specimens from 16 patients were analyzed in areas of former surgery in the retromolar wisdom tooth area; specimens from 16 patients were analyzed in the jawbone, apically of teeth with RF; and specimens from 19 patients were of the healthy jawbone. Each of the retromolar and the apical jawbone samples showed clinically fatty degenerated and osteonecrotic medullary changes.ResultsAll fatty necrotic and osteolytic jawbone (FDOJ) samples showed regulated on activation, normal T-cell expressed and secreted (RANTES) and fibroblast growth factor (FGF)-2 as the only extremely overexpressed cytokines. FDOJ cohorts showed a 30-fold mean overexpression of RANTES and a 20-fold overexpressed level of FGF-2 when compared to healthy controls.ConclusionsAs RANTES is discussed in the literature as a possible contributor to inflammatory diseases, and though it might have oncogenic effects, we hypothesize that FDOJ in areas of improper and incomplete wound healing in the jawbone might act as hyperactivated signaling pathways, while serving as an unknown source of “silent inflammation”. Because of the wide range of RANTES in immune diseases, treating FDOJ can cover many potential prediction or prognosis of individual outcomes.

Advanced [(18)F]FDG and [(11)C]flumazenil PET analysis for individual outcome prediction after temporal lobe epilepsy surgery for hippocampal sclerosis.

PurposeWe have previously shown that an imaging marker, increased periventricular [11C]flumazenil ([11C]FMZ) binding, is associated with failure to become seizure free (SF) after surgery for temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS). Here, we investigated whether increased preoperative periventricular white matter (WM) signal can be detected on clinical [18F]FDG-PET images. We then explored the potential of periventricular FDG WM increases, as well as whole-brain [11C]FMZ and [18F]FDG images analysed with random forest classifiers, for predicting surgery outcome.MethodsSixteen patients with MRI-defined HS had preoperative [18F]FDG and [11C]FMZ-PET. Fifty controls had [18F]FDG-PET (30), [11C]FMZ-PET (41), or both (21). Periventricular WM signal was analysed using Statistical Parametric Mapping (SPM8), and whole-brain image classification was performed using random forests implemented in R (http://www.r-project.org). Surgery outcome was predicted at the group and individual levels.ResultsAt the group level, non-seizure free (NSF) versus SF patients had periventricular increases with both tracers. Against controls, NSF patients showed more prominent periventricular [11C]FMZ and [18F]FDG signal increases than SF patients. All differences were more marked for [11C]FMZ. For individuals, periventricular WM signal increases were seen at optimized thresholds in 5/8 NSF patients for both tracers. For SF patients, 1/8 showed periventricular signal increases for [11C]FMZ, and 4/8 for [18F]FDG. Hence, [18F]FDG had relatively poor sensitivity and specificity. Random forest classification accurately identified 7/8 SF and 7/8 NSF patients using [11C]FMZ images, but only 4/8 SF and 6/8 NSF patients with [18F]FDG.ConclusionThis study extends the association between periventricular WM increases and NSF outcome to clinical [18F]FDG-PET, but only at the group level. Whole-brain random forest classification increases [11C]FMZ-PET's performance for predicting surgery outcome.

Predicting Survival of De Novo Metastatic Breast Cancer in Asian Women: Systematic Review and Validation Study

BackgroundIn Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia.Materials and MethodsWe performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic).ResultsWe identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48–0.53) to 0.63 (95% CI, 0.60–0.66).ConclusionThe discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making.

Renal Cell Carcinoma – How Can We Predict its Outcomes in Clinical Practice?

Summary Morbidity and mortality data of RCC (renal cell carcinoma) differs a lot among the European countries. In Latvia a growing trend in both incidence and mortality rates is still observed. The expanding availability of multiple treatment strategies has increased the importance of skilled individualized outcome prediction for patients. Several prognostic factors are available in RCC including anatomical, histological, clinical and molecular ones, but none of them is very precise, when used alone. Therefore increasing number of prognostic systems has been created in local and metastatic disease to increase predictive accuracy. In order to encourage the clinicians to use the available models in their routine practice, we tried to select the most relevant ones and include them in a simple algorithm to be used in common clinical scenarios throughout entire history of the disease in patients with RCC

Influence of a priori Information, Designs, and Undetectable Data on Individual Parameters Estimation and Prediction of Hepatitis C Treatment Outcome

Hepatitis C viral kinetic analysis based on nonlinear mixed effect models can be used to individualize treatment. For that purpose, it is necessary to obtain precise estimation of individual parameters. Here, we evaluated by simulation the influence on Bayesian individual parameter estimation and outcome prediction of a priori information on population parameters, viral load sampling designs, and methods for handling data below detection limit (BDL). We found that a precise estimation of both individual parameters and treatment outcome could be obtained using as few as six measurements in the first month of therapy. This result remained valid even when incorrect a priori information on population parameters was set as long as the parameters were identifiable and BDL data were properly handled. However, setting wrong values for a priori population parameters could lead to severe estimation/prediction errors if BDL data were ignored and not properly accounted in the likelihood function.

An Evidence-Based Checklist To Evaluate Neuropsychological Outcomes after Epilepsy Surgery (P01.045)

Objective: Using evidence-based medicine (EBM) principles, assemble and apply a checklist to evaluate validity and usefulness of neuropsychological (NP) outcomes of epilepsy surgery. Background The core tenet of EBM is the integration of best research evidence, clinical expertise and patient values. It is not clear to what extent these principles have been applied to NP research on outcomes after epilepsy surgery. Design/Methods: A checklist was derived from widely used criteria to assess the validity and usefulness of clinical research (e.g., JAMA Users9 Guides to the medical literature), NP methodological issues, and author experience. A systematic literature review yielded 148 eligible articles on epilepsy surgery and NP, of which 50% were selected for evaluation by two raters. Results: Inter-rater agreement was 89%. The 45-item checklist identified a lack of randomization and blinding (99%, 96% of articles). Most studies incorporated a surgical comparison group (75%), specified inclusion/exclusion criteria (76%), and considered group differences (73%). No studies reported a power calculation. The majority of studies used standardized NP measures (93%), with pre/post-surgical measures of change at sufficient follow-up (87%). Only a minority of articles used empirically-based measures of change (22%). The sample size at post-surgical assessment was consistently reported (93%), but reasons for loss to follow-up were not (35%). Patients were well-described on many variables (51% to 100%), but few studies reported ethnicity, socioeconomic status, antiepileptic medication, and psychiatric comorbidity (3% to 12%). Most studies provided clinically important information (73%) but not in a manner easily applicable to individual patients (22%). Conclusions: The EBM checklist identified threats to validity and clinical utility in NP epilepsy surgery research. Many of these threats could be addressed by applying more rigorous study design, attending to sample size and careful patient description, using empirically-based measures of change, and including methods that allow prediction of individual outcomes. Disclosure: Dr. Hrabok has nothing to disclose. Dr. Dykeman has nothing to disclose. Dr. Sherman has nothing to disclose. Dr. Wiebe has nothing to disclose.